Machine Learning Researcher, Audio

Bland AI·San Francisco·20 апр.

Machine Learning Researcher, Audio

🏢 ОфисMiddleПолная занятость

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Наша компания

At Bland.com, our mission is to empower enterprises to build AI phone agents at scale. Based in San Francisco, we are a fast-growing team reimagining how customers interact with businesses through voice. We have raised $65 million from leading Silicon Valley investors, including Emergence Capital, Scale Venture Partners, Y Combinator, and founders of Twilio, Affirm, and ElevenLabs. Voice is quickly becoming the primary interface between businesses and their customers. We are building the models and infrastructure that make those interactions feel natural, reliable, and genuinely human.

Чем предстоит заниматься

MACHINE LEARNING RESEARCHER, AUDIO

As a Machine Learning Researcher at Bland, you'll be working on foundational research and development across the core components of our voice stack: speech-to-text, large language models, neural audio codecs, and text-to-speech. Your work will define how our agents understand, reason, and speak in real time at enterprise scale

This is not a narrow research role. You will take ideas from theory to large-scale training to production inference systems serving millions of calls per day. You will design new modeling approaches, validate them with rigorous experimentation, and collaborate with engineering teams to deploy them into real customer environments

Design and train large scale text-to-speech models capable of expressive, controllable, human-sounding output

Develop neural audio codec-based TTS architectures for efficient, high-fidelity generation

Improve prosody modeling, question inflection, emotional expression, and multi-speaker robustness

Optimize for real-time, low-latency inference in production

Curate and process massive audio datasets across languages, speakers, and environments

Design staged training curricula and data filtering strategies

Scale training across distributed GPU clusters focusing on cost, throughput, and reliability

Наши требования

Experience with large scale distributed training

Research publications or open source contributions in speech or language AI

Background in real-time speech systems or telephony

PhD in ML, AI, or a related field, or equivalent research impact

Experience with self-supervised learning, multimodal modeling, or generative modeling

Ability to derive new formulations and implement them efficiently

Hands-on experience building or scaling TTS, STT, or neural audio codec systems

Familiarity with large scale speech datasets and real-world audio variability

Strong intuition for audio quality, prosody, and conversational dynamics

Experience training and serving large models on modern accelerators

Knowledge of inference optimization techniques, including quantization, kernel optimization, and memory efficiency

Understanding of real-time constraints in telephony or streaming environments

Мы предлагаем

Healthcare, dental, vision, all the good stuff

Meaningful equity in a fast-growing company

Every tool you need to succeed

Beautiful office in Jackson Square, SF with rooftop views

Competitive salary: $160,000 to $250,000

If you are energized by building and scaling TTS models, pioneering neural audio codecs, and pushing the boundaries of speech-to-text systems, we would love to hear from you

Дополнительно

Build and fine-tune large scale ASR systems robust to accents, noise, telephony artifacts, and code switching

Leverage self-supervised pretraining and large-scale weak supervision

Improve transcription accuracy for real-world enterprise scenarios, including structured extraction and conversational nuance

Research and implement neural audio codecs that achieve extreme compression with minimal perceptual loss

Explore discrete and continuous latent representations for scalable speech modeling

Design codec architectures that enable downstream generative modeling and controllable synthesis

Design ablation studies that isolate the impact of architectural changes

Measure improvements using both objective metrics and perceptual evaluations

Validate ideas quickly through focused experiments that confirm or eliminate hypotheses

WHAT MAKES YOU A GREAT FIT

Track record of designing controlled experiments and meaningful ablations

Comfortable working with both offline benchmarks and live production metrics

Ability to move quickly from hypothesis to validation

Comfortable in fast-moving startup environments

Strong ownership mindset from research through deployment

Excited by ambiguous, unsolved problems

You treat unsolved problems as opportunities to invent new paradigms

You identify the single experiment that can validate an idea in days, not months

You measure everything and let data drive decisions

You are obsessed with making voice agents sound truly human

You use AI tools aggressively to amplify your own impact and accelerate research cycles

Bland AI

San Francisco

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Опубликовано20 апр.

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